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检索条件"主题词=Distributed source coding"

共 384 条记录，以下是301-310 订阅

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A SYNDROME-BASED AUTOENCODER FOR POINT CLOUD GEOMETRY COMPRESSION

A SYNDROME-BASED AUTOENCODER FOR POINT CLOUD GEOMETRY COMPRE...

引用

IEEE International Conference on Image Processing (ICIP)

作者： Milani, Simone Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781728163956

Point cloud compression has been extensively-investigated in the past twenty years to find effective solutions that reduce the coded bit stream and permits adapting the coded bit rate to different scenarios. Despite these efforts, predictive strategies have so far performed poorly because of the low correlation level of the input data and the flexibility requirements, which imply minimizing the decoding dependences. The current paper proposes a convolutional autoencoder that applies the principles of distributed source coding (DSC) to the deep representations of voxelized point cloud geometry data. The hidden variables, called syndromes, enable reconstructing the coded point cloud geometry from different reference data. The proposed strategy overcomes the state-of-the-art solutions in terms of flexibility and rate-distortion performance.

关键词： point cloud compression geometry distributed source coding autoencoder UNet

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Joint coding/Routing Optimization for Correlated sources in Wireless Visual Sensor Networks

Joint Coding/Routing Optimization for Correlated Sources in ...

引用

IEEE Global Telecommunications Conference (GLOBECOM 09)

作者： Li, Chenglin Zou, Junni Xiong, Hongkai Zhang, Yongsheng Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Univ Dept Communicat Engn Shanghai 200079 Peoples R China

ISBN: (纸本)9781424441471

This paper studies a joint coding/routing optimization between network lifetime and rate-distortion, by applying information theory to wireless visual sensor networks for correlated sources. Arbitrary coding (distributed source coding and network coding) from both combinatorial optimization and information theory could make significant progress towards the performance limit of information networks and tractable. Also, multipath routing can spread energy utilization across nodes within the entire network to keep a potentially longer lifetime, and solve the wireless contention issues by the splitting traffic. The objective function not only keeps a total energy consumption of encoding power, transmission power, and reception power minimized, but ensures the information received by sink nodes to approximately reconstruct the visual field. Based on the localized Slepian-Wolf coding and network coding-based multipath routing, the balance problem between distortion (capacity) and lifetime (costs) is modeled as an optimization formulation with a distributed solution. Through a primal decomposition, a two-level optimization is relaxed with Lagrangian dualization and solved with the gradient algorithm. The low-level optimization problem is decomposed into a secondary master dual problem (encoding, energy, and congestion prices update) with four cross-layer subproblems: a rate control problem, a channel contention problem, a distortion control problem, and an energy conservation problem. Numerical results validate the convergence and performance of the proposed algorithm.

关键词： Wireless visual sensor network distributed source coding network coding network lifetime rate-distortion

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A Modified Bethe Free Energy Approximation for Codeword Quantization

A Modified Bethe Free Energy Approximation for Codeword Quan...

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47th Annual Conference on Information Sciences and Systems (CISS)

作者： Fernandez, Jose M. Regalia, Phillip A. Catholic Univ Amer Washington DC 20064 USA Catholic Univ Amer Dept Elect & Comp Engn Washington DC 20064 USA

ISBN: (纸本)9781467352390;9781467352376

A novel codeword quantization algorithm based on message-passing using a low density generator matrix formulation is proposed and analyzed. The scheme is a seemingly subtle variant on a recently proposed "truthiness" propagation algorithm, but one which affords a more explicit connection to a modified Bethe free energy function. Applications to distributed coding in sensor networks are also included in the simulation examples, where the algorithm is observed to outperform conventional LDPC belief propagation decoding using side information, in a practical setting when the reliability of the side information diminishes.

关键词： belief propagation Bethe free energy codeword quantization distributed source coding LDGM LDPC MTP truthiness propagation

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Efficient improvement of Side Information in GOB-based DVC System

Efficient improvement of Side Information in GOB-based DVC S...

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IEEE International Symposium on Circuits and Systems

作者： Wu, Tsung-Che Hsu, Ji-Hua Lee, Chang-Ming Chiang, Jui-Chiu Natl Chung Cheng Univ Dept Commun Engn Chiayi Taiwan

ISBN: (纸本)9781467302197

Among the emerging video coding schemes, the effectual solution for the separate-encoding and joint-decoding architecture is the distributed source coding (DSC). In the DSC-based video system, the side information is available in the Wyner-Ziv (WZ) decoder and video reconstruction. Theoretically, the quality of side information (SI), usually referring to the difference between SI and source, dominates the coding efficiency. In order to improve the coding efficiency of DVC with temporal group of blocks, we proposed three SI generations after analyzing the correlation between the SI and original information. In contrast to the traditional improvement in the DVC decoder, the complexity of SI generator was dramatically reduced in our schemes. Experimental results show that the best method can reduce 0.7% bit-rate with less computation and no codec delay compared to the bi-linear interpolation (BLI) method.

关键词： distributed source coding Wyner-Ziv theorem side information interpolation correlation noise modeling

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distributed QUANTIZATION FOR COMPRESSED SENSING

DISTRIBUTED QUANTIZATION FOR COMPRESSED SENSING

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Shirazinia, Amirpasha Chatterjee, Saikat Skoglund, Mikael KTH Royal Inst Technol ACCESS Linnaeus Ctr Commun Theory Dept Stockholm Sweden

ISBN: (纸本)9781479928934

We study distributed coding of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a distributed framework for realizing optimized quantizer that enables encoding CS measurements of correlated sparse sources followed by joint decoding at a fusion center. The optimality of VQ encoder-decoder pairs is addressed by minimizing the sum of mean-square errors between the sparse sources and their reconstruction vectors at the fusion center. We derive a lower-bound on the end-to-end performance of the studied distributed system, and propose a practical encoder-decoder design through an iterative algorithm.

关键词： Compressed sensing distributed source coding vector quantization correlation mean square error

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Two Frameworks for Joint Compressing and Recovering Destructed Signals in Wireless Multimedia Sensor Networks 22

Two Frameworks for Joint Compressing and Recovering Destruct...

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22nd Iranian Conference on Electrical Engineering (ICEE)

作者： Eslami, Mohammad Torkamani-Azar, Farah Mehrshahi, Esfandiar Shahid Beheshti Univ Fac Elect & Comp Engn Cognit Telecommun Res Grp GC Tehran Iran Shahid Beheshti Univ Fac Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781479944095

The WSNs are developed to sense, gather, process and transmit the real-world information and so in recent years, solving numerous challenges of wireless sensor networking are considered intensely. In this paper based on compressive sensing two frameworks denoted as IndM and ECM are proposed to compress and reconstruct the signals of the sensors even for networks which the data transmission is imperfect. In addition, in Wireless Multimedia Sensor Networks intra-and inter-signal correlations can be exploited in the theory of distributed source coding and similarly in distributed compressive sensing to compress signals as much as possible. These cases may be occurred in applications and services work with smart spaces and context aware networks. While IndM reconstructs the signals of sensors separately, ECM method uses the concepts of distributed compressive sensing and the shared information between sensor's signals to compress the signals more. Compressing the sensors' signals based on proposed methods in WMSN applications brings lower amount of transmitted data and therefore lower bandwidth usage. In addition, because of exploiting compressive sensing sampling method to compress the signals and this sampling scheme is too simple, using proposed methods brings lower computational cost and also more lifetime in sensor side. Furthermore, all of these achievements can be attain in imperfect transmission system.

关键词： Wireless Multimedia Sensor Networks Sparsity Representation Signal Compression Compressive Sensing distributed source coding Joint Sparsity Model

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An adaptive distributed data aggregation based on RCPC for wireless sensor networks

An adaptive distributed data aggregation based on RCPC for w...

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Conference on Sensors, and Command, Control, Communications, and Intelligence (C31) Technologies for Homeland Secuity and Homeland Defense V

作者： Hua, Guogang Chen, Chang Wen Florida Inst Technol Dept Elect & Comp Engn Melbourne FL 32901 USA

ISBN: (纸本)0819462578

One of the most important design issues in wireless sensor networks is energy efficiency. Data aggregation has significant impact on the energy efficiency of the wireless sensor networks. With massive deployment of sensor nodes and limited energy supply, data aggregation has been considered as an essential paradigm for data collection in sensor networks. Recently, distributed source coding has been demonstrated to possess several advantages in data aggregation for wireless sensor networks. distributed source coding is able to encode sensor data with lower bit rate without direct communication among sensor nodes. To ensure reliable and high throughput transmission with the aggregated data, we proposed in this research a progressive transmission and decoding of Rate-Compatible Punctured Convolutional (RCPC) coded data aggregation with distributed source coding. Our proposed 1/2 RSC codes with Viterbi algorithm for distributed source coding are able to guarantee that, even without any correlation between the data, the decoder can always decode the data correctly without wasting energy. The proposed approach achieves two aspects in adaptive data aggregation for wireless sensor networks. First, the RCPC coding facilitates adaptive compression corresponding to the correlation of the sensor data. When the data correlation is high, higher compression ration can be achieved. Otherwise, lower compression ratio will be achieved. Second, the data aggregation is adaptively accumulated. There is no waste of energy in the transmission;even there is no correlation among the data, the energy consumed is at the same level as raw data collection. Experimental results have shown that the proposed distributed data aggregation based on RCPC is able to achieve high throughput and low energy consumption data collection for wireless sensor networks.

关键词： wireless sensor networks data aggregation adaptive distributed source coding RCPC

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An extremal inequality motivated by multiterminal information-theoretic problems

An extremal inequality motivated by multiterminal informatio...

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IEEE International Symposium on Information Theory

作者： Liu, Tie Viswanath, Pramod Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

ISBN: (纸本)1424405041

We prove a new extremal inequality, motivated by the vector Gaussian broadcast channel and the distributed source coding with a single quadratic distortion constraint problems. As a corollary, this inequality yields a generalization of the classical entropy-power inequality (EPI). As another corollary, this inequality sheds insight into maximizing the differential entropy of the sum of two dependent random variables.

关键词： differential entropy distributed source coding entropy-power inequality (EPT) Fisher information vector Gaussian broadcast channel

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Joint Opportunistic Network coding and Opportunistic Routing for Correlated Data Gathering in Wireless Sensor Network

Joint Opportunistic Network Coding and Opportunistic Routing...

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IEEE 78th Vehicular Technology Conference (VTC Fall)

作者： Tan, Chong Zou, Junni Wang, Min Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200072 Peoples R China Shanghai Univ Dept Commun Engn Shanghai 200072 Peoples R China

ISBN: (纸本)9781467361873

In this paper, we study the problem of correlated data gathering in wireless sensor networks. An opportunistic routing protocol joint with opportunistic network coding is proposed for correlated data gathering. In order to reduce the total transmission, we introduce the expected number of coded transmission (ECTX) as metric for the routing selection. Moreover, we also define the coding gain for the network nodes to count the coding opportunistic, with which the throughput of the wireless sensor network is further increased. Numerical simulations are conducted for performance analysis with the proposed routing scheme.

关键词： Wireless sensor network opportunistic network coding opportunistic routing correlated data gathering distributed source coding

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distributed Compression in the Era of Machine Learning: A Review of Recent Advances 58

Distributed Compression in the Era of Machine Learning: A Re...

引用

58th Annual Conference on Information Sciences and Systems (CISS)

作者： Ozyilkan, Ezgi Erkip, Elza NYU Dept Elect & Comp Engn Brooklyn NY 11201 USA

ISBN: (纸本)9798350369298;9798350369304

Many applications from camera arrays to sensor networks require efficient compression and processing of correlated data, which in general is collected in a distributed fashion. While information-theoretic foundations of distributed compression are well investigated, the impact of theory in practice-oriented applications to this day has been somewhat limited. As the field of data compression is undergoing a transformation with the emergence of learning-based techniques, machine learning is becoming an important tool to reap the long-promised benefits of distributed compression. In this paper, we review the recent contributions in the broad area of learned distributed compression techniques for abstract sources and images. In particular, we discuss approaches that provide interpretable results operating close to information-theoretic bounds. We also highlight unresolved research challenges, aiming to inspire fresh interest and advancements in the field of learned distributed compression.

关键词： distributed source coding Wyner-Ziv coding lossy compression binning neural networks rate-distortion theory learning

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